Artificial intelligence based imaging systems and methods for interacting with individuals via a web environment

ABSTRACT

Artificial intelligence based systems and methods are described for interacting with individuals via a web environment. A provisioning server is configured to receive a request from a web browser, executing on a client device, for a predefined imaging code stored in a memory of the provisioning server. The provisioning server responds to the request by transferring the predefined imaging code to the web browser. The predefined imaging code is configured to be executed by the client device to render an interactive graphical user interface (GUI) within the web browser on a display of the client device; load, into a memory of the client device, one or more images of an individual; determine, based on image analysis of the one or more images of the individual, one or more personal attributes of the individual; and, render the one or more personal attributes of the individual within the interactive GUI.

FIELD

The present disclosure generally relates to artificial intelligencebased imaging systems and methods, and more particularly to, artificialintelligence based imaging systems and methods for interacting withindividuals via a web environment.

BACKGROUND

Conventional web based communication typically requires a web browser,or a native application (app) executing on a native operating system ofa client device, to make multiple back-and-forth, round-tripcommunications with servers in order to operate. Such communications aretypically required for client devices to offload complicated tasks,which can require extensive processing and memory resources, to theserver because the server typically has more computing resources tohandle such tasks when compared with those of conventional clientdevices. Such back-and-forth, round-trip communications, however, cancreate several problems related to performance and security. First,performance of a conventional web browser or native app is dependent onthe speed, latency, and potential errors of the computer network throughwhich it communicates, where a web browser or native app can fail toload or operate over a poor or compromised network or connection. Inaddition, a poor connection can cause a conventional web application torun at a reduced speed or capacity. This is especially so for softwareapplications that transmit or use large sized files or information, suchas image(s) or videos. For example, transfer of images can cause networkdelays because images and/or videos generally require high networkbandwidth, and without it, delays and/or errors can be experienced atboth the client and server sides. While some native apps can processimages, such native apps are not desirable because they require at leasttwo different operating systems for development and require checking forcompatibility of multiple devices with varying hardware across multipleoperating systems in order to operate effectively. For these reasons,native apps can be error prone, where a native app is not maintained,and can otherwise require constant versioning and maintenance, and mustbe checked and approved by a third-party platform distributor (e.g., theAPPLE App store or GOOGLE Play store), that may require distributionfees. Moreover, consumers are increasingly unwilling to downloadnon-essential native apps, which further decreases the desirability ofsuch native apps.

Additional problems arise because conventional web based communicationsare not secure. While security based protocols, such as Transport LayerSecurity (TLS) and secure sockets layer (SSL), can increase the securityof web based communications, such protocols are not fail safe, and canbe hacked or cracked, allowing a cyber-attacker to gain access tounderlying information of intercepted web based communications. Suchcyber-attacks are especially problematic for personal information (e.g.,personal identifiable information (PII)) where users, and the companieswhich provide websites or servers with which users communicate, aretypically extremely sensitive about protection and privacy of thepersonal information. This is especially so given that personalinformation is becoming increasingly important as it is subject to agrowing body of privacy regulations.

For the foregoing reasons, there is a need for artificial intelligencebased imaging systems and methods for interacting with individuals via aweb environment.

SUMMARY

Generally, the artificial intelligence based imaging systems and methodsfor interacting with individuals via a web environment described hereinprovide a more secure environment for processing private information,such as PII. Generally, as referred to herein, PII is data that couldpotentially be used to identify a particular individual or person.Examples of PII include a full name, a Social Security number, adriver's license number, a bank account number, a passport number,and/or an email address of an individual. PII can also include an imageof an individual. For example, such PII may include such as “selfie”images.

With the artificial intelligence based imaging systems and methodsdescribed herein, private information need not be communicated over acomputer network, and therefore, is not subject to interception by acyber-attack. In particular, the disclosed systems and methods are ableto operate without a need for constant connection to the internet, whereonly an initial connection or transmission (which does not necessarilyinvolve the transfer of private information) is required to transfer apredefined imaging code to run on a client device. Any privateinformation, such as PII, may be stored or processed locally on a user'sclient device by the predefined imaging code. In addition, the disclosedartificial intelligence based imaging systems and methods provide morepowerful functionality by providing artificial intelligence imageanalysis to enhance, annotate, or otherwise perform image analysis onimages (e.g., selfies) for making user and/or product recommendationsspecific to the user.

More specifically, as described herein, an artificial intelligence basedimaging system is configured to interact with individuals via a webenvironment. The artificial intelligence based imaging system comprisesa provisioning server comprising one or more processors and one or morememories. The provisioning server may be configured to respond torequests from a web browser executing on a client device. In addition, apredefined imaging code may be stored in the one or more memories of theserver. Computing instructions may be configured to execute on the oneor more processors of the provisioning server. The computinginstructions may cause the provisioning server, upon receiving a requestfrom the web browser, to transfer the predefined imaging code to the webbrowser. The predefined imaging code may be configured to be stored in amemory of the client device by the web browser when the predefinedimaging code is received by the web browser. In addition, the predefinedimaging code may be configured to be executed by a client processor ofthe client device upon the predefined imaging code being received by theweb browser. The predefined imaging code may be configured, uponexecution by the client processor, to render an interactive graphicaluser interface (GUI) within the web browser on a display of the clientdevice; load, into a memory of the client device, one or more images ofan individual; determine, based on image analysis of the one or moreimages of the individual, one or more personal attributes of theindividual; and, render the one or more personal attributes of theindividual within the interactive GUI. Additionally or alternatively, insome embodiments, the predefined imaging code is executable within theweb browser without communication with the provisioning server. Stillfurther, additionally or alternatively, in some embodiments, thepredefined imaging code may be cached in the memory of the client devicewhere the predefined imaging code allowed to access at least a portionof the memory as allocated for the web browser.

In addition, as described herein, an artificial intelligence basedimaging method is disclosed for interacting with individuals via a webenvironment. The artificial intelligence based imaging method comprisesreceiving, at a provisioning server, a request from a web browser for apredefined imaging code, the provisioning server having access to amemory storing the predefined imaging code. The intelligence basedimaging method further comprises responding, by the provisioning server,to the request by transferring the predefined imaging code to the webbrowser. The web browser may execute on a client device. The predefinedimaging code may be configured to be stored in a memory of the clientdevice by the web browser when the predefined imaging code is receivedby the web browser. In addition, the predefined imaging code may beconfigured to be executed by a client processor of the client deviceupon the predefined imaging code being received by the web browser. Theartificial intelligence based imaging method may further compriserendering, with the predefined imaging code, a GUI within the webbrowser on a display of the client device. The artificial intelligencebased imaging method may further comprise loading, with the predefinedimaging code into a memory of the client device, one or more images ofan individual. The artificial intelligence based imaging method mayfurther comprise determining, with the predefined imaging code based onimage analysis of the one or more images of the individual, one or morepersonal attributes of the individual. The artificial intelligence basedimaging method may further comprise rendering, with the predefinedimaging code, the one or more personal attributes of the individualwithin the interactive GUI. Additionally, or alternatively, in someembodiments the predefined imaging code may be executable within the webbrowser without communication with the provisioning server. Stillfurther, additionally or alternatively, in some embodiment, thepredefined imaging code may be cached in the memory of the clientdevice, the predefined imaging code allowed to access at least a portionof the memory as allocated for the web browser.

In addition, as described herein, an artificial intelligence basedimaging system is configured to interact with individuals via a webenvironment. The artificial intelligence based imaging system comprisesa provisioning server comprising one or more processors and one or morememories. The provisioning server may be configured to respond torequests from a web browser executing on a client device. In addition,the predefined imaging code may be configured to be stored in the one ormore memories of the server. Still further, computing instructions maybe configured to execute on the one or more processors of theprovisioning server. The computing instructions may cause theprovisioning server, upon receiving a request from the web browser, totransfer the predefined imaging code to the web browser. In addition,the predefined imaging code may be configured to be stored in a memoryof the client device by the web browser when the predefined imaging codeis received by the web browser. Still further, the predefined imagingcode may be configured to be executed by a client processor of theclient device upon the predefined imaging code being received by the webbrowser. The predefined imaging code may be configured, upon executionby the client processor, to render a GUI within the web browser on adisplay of the client device; load, into a memory of the client device,one or more images of an individual; determine, based on image analysisof the one or more images of the individual, one or more personalattributes of the individual; and, render the one or more personalattributes of the individual within the interactive GUI. Additionally oralternatively, in some embodiments, the one or more personal attributesmay comprise one or more facial features, one or more oral features, orone or more hair-based features of the individual.

Further, as described herein, an artificial intelligence based imagingmethod is disclosed for interacting with individuals via a webenvironment. The artificial intelligence based imaging method comprisesreceiving, at a provisioning server, a request from a web browser for apredefined imaging code, the provisioning server having access to amemory storing the predefined imaging code. The intelligence basedimaging method further comprises responding, by the provisioning server,to the request by transferring the predefined imaging code to the webbrowser. The web browser may execute on a client device. The predefinedimaging code may be configured to be stored in a memory of the clientdevice by the web browser when the predefined imaging code is receivedby the web browser. In addition, the predefined imaging code may beconfigured to be executed by a client processor of the client deviceupon the predefined imaging code being received by the web browser. Theartificial intelligence based imaging method may further compriserendering, with the predefined imaging code, a GUI within the webbrowser on a display of the client device. The artificial intelligencebased imaging method may further comprise loading, with the predefinedimaging code into a memory of the client device, one or more images ofan individual. The artificial intelligence based imaging method mayfurther comprise determining, with the predefined imaging code based onimage analysis of the one or more images of the individual, one or morepersonal attributes of the individual. The artificial intelligence basedimaging method may further comprise rendering, with the predefinedimaging code, the one or more personal attributes of the individualwithin the interactive GUI. Additionally or alternatively, in someembodiments, the one or more personal attributes may comprise one ormore facial features, one or more oral features, or one or morehair-based features of the individual.

In accordance with the above, and with the disclosure herein, thepresent disclosure includes improvements in computer functionality or inimprovements to other technologies at least because the disclosuredescribes that, e.g., a client device is improved where the security ofthe client device is enhanced by the use of predefined imaging code. Thepredefined imaging code, once received and executed by the clientdevice, is self-sufficient and does not transmit private or personalinformation of a user over a public network (e.g., the Internet). Thisprevents such information from being intercepted by cyber-attackers.That is, the present disclosure describes improvements in thefunctioning of the computer itself or “any other technology or technicalfield” because the data is secure as it does not need to be sent throughnetworks with inherent security vulnerabilities. This improves over theprior art at least because conventional web communications are prone tosecurity issues/cyber-attacks because such web communications may beintercepted and cracked, even if sent by secure protocol standards suchas SSL and/or TLS.

In addition, the present disclosure includes improvements in computerfunctionality or in improvements to other technologies at least becausethe disclosure describes that, e.g., a client device can provide,through use of the predefined imaging code, higher resolution imaginganalysis by executing on the client device alone or at least in largepart. These advantages can be particularly important for apps whereimages and other personal information is necessary for the app to workand where the app uses large files (e.g. images) and requiressophisticated processing. Faster data processing is achieved because,with the use of the predefined imaging code, the client device no longerexperiences lag from typical network communications, especially viatransmission of large files or data, e.g., image data. Instead, with thepredefined imaging code, a client device has no need for permanentinternet connection. In various embodiments, after transmitted andloaded (e.g., once before every use), the predefined imaging code canthen operate with no or little data connection to a server.

In addition, the present disclosure includes specific features otherthan what is well-understood, routine, conventional activity in thefield, or adding unconventional steps that confine the claim to aparticular useful application, e.g., artificial intelligence basedimaging systems and methods for interacting with individuals via a webenvironment as described herein.

Advantages will become more apparent to those of ordinary skill in theart from the following description of the preferred embodiments whichhave been shown and described by way of illustration. As will berealized, the present embodiments may be capable of other and differentembodiments, and their details are capable of modification in variousrespects. Accordingly, the drawings and description are to be regardedas illustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of the system andmethods disclosed therein. It should be understood that each Figuredepicts an embodiment of a particular aspect of the disclosed system andmethods, and that each of the Figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals.

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and instrumentalities shown,wherein:

FIG. 1 illustrates an example artificial intelligence based imagingsystem configured to interact with individuals via a web environment inaccordance with various embodiments disclosed herein.

FIG. 2 illustrates a diagram of an example client device implementingpredefined imaging code as received from a provisioning server of theartificial intelligence based imaging system of FIG. 1 in accordancewith various embodiments disclosed herein.

FIG. 3 illustrates a diagram of an example artificial intelligence basedimaging method for interacting with individuals via a web environment inaccordance with various embodiments disclosed herein.

FIG. 4 illustrates a diagram of a further example artificialintelligence based imaging method for interacting with individuals via aweb environment in accordance with the method of FIG. 3, and inaccordance with various embodiments disclosed herein, and where one ormore personal attributes of an individual comprise one or more facialfeatures, one or more oral features, or one or more hair-based featuresof the individual.

FIG. 5 illustrates an example image of any of the artificialintelligence based imaging systems or methods as described for FIGS.1-4, comprising an individual showing one or more personal attributes ofthe individual comprising one or more facial features, one or more oralfeatures, or one or more hair-based features of the individual, inaccordance with various embodiments disclosed herein.

FIG. 6 illustrates an example display or user interface for recommendingone or more products based on the one or more personal attributes of theindividual of FIG. 5.

The Figures depict preferred embodiments for purposes of illustrationonly.

Alternative embodiments of the systems and methods illustrated hereinmay be employed without departing from the principles of the inventiondescribed herein.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates an example artificial intelligence based imagingsystem 100 configured to interact with individuals via a web environmentin accordance with various embodiments disclosed herein. In the exampleembodiment of FIG. 1, artificial intelligence based imaging system 100includes server(s) 102, which may comprise one or more computer servers.In various embodiments server(s) 102 comprise multiple servers, whichmay comprise a multiple, redundant, or replicated servers as part of aserver farm. In still further embodiments, server(s) 102 may beimplemented as cloud-based servers. For example, server(s) 102 may beany one or more cloud-based platform(s) such as MICROSOFT AZURE, AMAZONAWS, or the like. Server(s) 102 may include one or more processor(s) 104as well as one or more computer memories 106. Server(s) 102 may bereferred to herein as “provisioning server(s).”

The memories 106 may include one or more forms of volatile and/ornon-volatile, fixed and/or removable memory, such as read-only memory(ROM), electronic programmable read-only memory (EPROM), random accessmemory (RAM), erasable electronic programmable read-only memory(EEPROM), and/or other hard drives, flash memory, MicroSD cards, andothers. The memorie(s) 106 may store an operating system (OS) (e.g.,Microsoft Windows, Linux, Unix, etc.) capable of facilitating thefunctionalities, apps, methods, or other software as discussed herein.The memorie(s) 106 may also store a predefined imaging code 108 fortransmission or transfer to web browsers or client devices as describedherein. Additionally, or alternatively, the predefined imaging code 108may also be stored in database 105, which is accessible or otherwisecommunicatively coupled to server(s) 102. The memories 106 may alsostore machine readable instructions, including any of one or moreapplication(s), one or more software component(s), and/or one or moreapplication programming interfaces (APIs), which may be implemented tofacilitate or perform the features, functions, or other disclosuredescribed herein, such as any methods, processes, elements orlimitations, as illustrated, depicted, or described for the variousflowcharts, illustrations, diagrams, figures, and/or other disclosureherein. For example, at least some of the applications, softwarecomponents, or APIs may be, include, otherwise be part of, an imagingbased machine learning model or component, and/or the predefined imagingcode 108, where each are configured to facilitate their variousfunctionalities discussed herein. It should be appreciated that one ormore other applications may be envisioned and that are executed by theprocessor(s) 104.

The processor(s) 104 may be connected to the memories 106 via a computerbus responsible for transmitting electronic data, data packets, orotherwise electronic signals to and from the processor(s) 104 andmemories 106 in order to implement or perform the machine readableinstructions, methods, processes, elements or limitations, asillustrated, depicted, or described for the various flowcharts,illustrations, diagrams, figures, and/or other disclosure herein.

The processor(s) 104 may interface with the memory 106 via the computerbus to execute the operating system (OS). The processor(s) 104 may alsointerface with the memory 106 via the computer bus to create, read,update, delete, or otherwise access or interact with the data stored inthe memories 106 and/or the database 104 (e.g., a relational database,such as Oracle, DB2, MySQL, or a NoSQL based database, such as MongoDB).The data stored in the memories 106 and/or the database 104 may includeall or part of any of the data or information described herein,including, for example, the one or more search requests, the one or moretransaction details, and the profile information of the user.

The server(s) 102 may further include a communication componentconfigured to communicate (e.g., send and receive) data via one or moreexternal/network port(s) to one or more networks or local terminals,such as computer network 120 and/or terminal 109 (for rendering orvisualizing) described herein. In some embodiments, server(s) 102 mayinclude a client-server platform technology such as ASP.NET, Java J2EE,Ruby on Rails, Node.js, a web service or online API, responsive forreceiving and responding to electronic requests. The server(s) 102 mayimplement the client-server platform technology that may interact, viathe computer bus, with the memories(s) 106 (including theapplications(s), component(s), API(s), data, etc. stored therein) and/ordatabase 105 to implement or perform the machine readable instructions,methods, processes, elements or limitations, as illustrated, depicted,or described for the various flowcharts, illustrations, diagrams,figures, and/or other disclosure herein. According to some embodiments,the server(s) 102 may include, or interact with, one or moretransceivers (e.g., WWAN, WLAN, and/or WPAN transceivers) functioning inaccordance with IEEE standards, 3GPP standards, or other standards, andthat may be used in receipt and transmission of data viaexternal/network ports connected to computer network 120. In someembodiments, computer network 120 may comprise a private network orlocal area network (LAN). Additionally, or alternatively, computernetwork 120 may comprise a public network such as the Internet.

Server(s) 102 may further include or implement an operator interfaceconfigured to present information to an administrator or operator and/orreceive inputs from the administrator or operator. As shown in FIG. 1,an operator interface may provide a display screen (e.g., via terminal109). Server(s) 102 may also provide I/O components (e.g., ports,capacitive or resistive touch sensitive input panels, keys, buttons,lights, LEDs), which may be directly accessible via or attached toserver(s) 102 or may be indirectly accessible via or attached toterminal 109. According to some embodiments, an administrator oroperator may access the server 102 via terminal 109 to reviewinformation, make changes, input training data, and/or perform otherfunctions.

As described above herein, in some embodiments, server(s) 102 mayperform the functionalities as discussed herein as part of a “cloud”network or may otherwise communicate with other hardware or softwarecomponents within the cloud to send, retrieve, or otherwise analyze dataor information described herein.

In general, a computer program or computer based product, application,or code (e.g., the predefined imaging code or other computinginstructions described herein) may be stored on a computer usablestorage medium, or tangible, non-transitory computer-readable medium(e.g., standard random access memory (RAM), an optical disc, a universalserial bus (USB) drive, or the like) having such computer-readableprogram code or computer instructions embodied therein, wherein thecomputer-readable program code or computer instructions may be installedon or otherwise adapted to be executed by the processor(s) 104 (e.g.,working in connection with the respective operating system in memories106) to facilitate, implement, or perform the machine readableinstructions, methods, processes, elements or limitations, asillustrated, depicted, or described for the various flowcharts,illustrations, diagrams, figures, and/or other disclosure herein. Inthis regard, the program code may be implemented in any desired programlanguage, and may be implemented as machine code, assembly code, bytecode, interpretable source code or the like (e.g., via Golang, Python,C, C++, C #, Objective-C, Java, Scala, ActionScript, JavaScript, HTML,CSS, XML, etc.).

As shown in FIG. 1, server(s) 102 are communicatively connected, viacomputer network 120 to the one or more client devices 111 c 1-111 c 3and/or 112 c 1-112 c 3 via base stations 111 b and 112 b. In someembodiments, base stations 111 b and 112 b may comprise cellular basestations, such as cell towers, communicating to the one or more clientdevices 111 c 1-111 c 3 and 112 c 1-112 c 3 via wireless communications121 based on any one or more of various mobile phone standards,including NMT, GSM, CDMA, UMMTS, LTE, 5G, or the like. Additionally oralternatively, base stations 111 b and 112 b may comprise routers,wireless switches, or other such wireless connection pointscommunicating to the one or more client devices 111 c 1-111 c 3 and 112c 1-112 c 3 via wireless communications 122 based on any one or more ofvarious wireless standards, including by non-limiting example, IEEE802.11a/b/c/g, the BLUETOOTH standard, or the like.

Any of the one or more client devices 111 c 1-111 c 3 and/or 112 c 1-112c 3 may comprise a cellular phone, a mobile phone, a tablet device, apersonal data assistance (PDA), or the like, including, by non-limitingexample, an APPLE iPhone or iPad device or a GOOGLE ANDROID based mobilephone or table. In addition, the one or more client devices 111 c 1-111c 3 and/or 112 c 1-112 c 3 may implement or execute an operating system(OS) or mobile platform such as Apple's iOS and/or Google's Androidoperation system. Any of the one or more client devices 111 c 1-111 c 3and/or 112 c 1-112 c 3 may comprise one or more processors and/or one ormore memories for storing, implementing, or executing computinginstructions or code (e.g., the predefined imaging code) as described invarious embodiments herein.

FIG. 2 illustrates a diagram of an example client device 200implementing predefined imaging code 108 as received from a provisioningserver 102 of the artificial intelligence based imaging system 100 ofFIG. 1 in accordance with various embodiments disclosed herein. Clientdevice 200 could be any of the client devices 111 c 1-111 c 3 and/or 112c 1-112 c 3 as described herein for FIG. 1. In FIG. 2, client device 200is illustrated as a mobile phone, e.g., such as an Apple iPhone deviceor a Google Android device. However, it is to be understood that clientdevice 200 could be a tablet, PDA, or other such similar client device,for example, as described herein.

In the example of FIG. 2, Client device 200 includes a CPU 202 (e.g., aprocessor) and a camera 204. Camera 204 is configured to take, capture,or otherwise generate digital images (e.g., pixel based images) and, atleast in some embodiments, may store such images in a memory 203 ofclient device 200. Memory 203 is communicatively coupled to oraccessible by CPU 202. The CPU 202 may implement or execute computinginstructions, including instructions to implement web browser 210 on adisplay screen 201 of client device 200. In a various embodiments, webbrowser 210 may be implemented as a mobile application (app) or nativeapp for the given operating system of client device 200. Use of webbrowser 210 provides very wide distribution and/or availability of theartificial intelligence based imaging systems and methods to drivetraffic and use of the artificial intelligence based imaging systems andmethods describe herein. That is, web browser 210 allows implementationand execution of the artificial intelligence based imaging systems andmethods regardless of operating system or client device model, e.g.,client device 200.

Client device 200 may comprise a wireless transceiver to receivewireless communications 121 and/or 122 from base stations 111 b and/or112 b, as described herein. In various embodiments described herein,client device 200 and/or web browser 210 may implement or executeinstructions to request predefined imaging code 108 from provisioningserver 102. For example, as shown for FIG. 2, in some embodiments,predefined imaging code 108 may be transferred to client device 200and/or web browser 210 as a script, such as, by non-limiting example, aJavaScript (.js) file, a web assembly module (WASM), and/or the like. Invarious embodiments, predefined imaging code 108 may be stored or cachedin memory 203 of client device 200. In some embodiments, predefinedimaging code 108 may further comprise, implement, have access to,render, or otherwise expose, at least in part, graphical user interface(GUI) 218 for displaying text and/or images on display screen 201.Additionally, or alternatively, web browser 210 may comprise, implement,have access to, render, or otherwise expose, at least in part, GUI 218for displaying text and/or images on display screen 201.

The predefined imaging code 108 provides imaging analysis and/or imagingalgorithms on digital images captured by camera 204. For example, invarious embodiments, such imaging analysis and/or imaging algorithms maybe implemented by predefined or compiled code of predefined imaging code108. Such code may include, but is not limited, to web assembly (WASM).Generally, WASM is a portable binary-code format for generation ofexecutable programs that can be executed or implemented in a hostenvironment such as a web browser (e.g., web browser 210). Because theWASM is an executable program, it provides high performance within theweb browsers compared to traditional browser based technologies,including, for example, stand-alone JavaScript and cascading stylesheets (CSS). Additionally or alternatively, predefined imaging code 108may also comprise scripts or code, such as code from third-partylibraries such as the TENSORFLOW library (e.g., Tensorflow.js). Forexample, the TENSORFLOW library provides predefined imaging code 108with artificial intelligence algorithms for analyzing the digital imagesas described herein. Additionally, or alternatively, predefined imagingcode 108 includes proprietary JavaScript and/CSS that loads, caches, orotherwise interacts with WASM code and/or third party code (e.g.,TENSORFLOW). In various embodiments, predefined imaging code 108includes each of one or more of WASM and JavaScript code (e.g.,Tensorflow.js and/or proprietary JavaScript) for implementing themethods, flowcharts, algorithms, or portions thereof, as describedherein.

As shown in FIG. 2, in various embodiments, a camera 204 is may be used(e.g., as requested from GUI 218) to capture digital images, such as“selfie” 220, e.g., a self-taken picture. It is to be understood thatwhile selfies are described for FIG. 2, other digital images arecontemplated herein (e.g., pictures taken by persons other than thetarget individual). In some embodiments, as shown for FIG. 2, “selfie”220 may be optionally displayed via GUI 218. Additionally, oralternatively, “selfie” 220 may be provided to predefined imaging code108 for imaging analysis which may include, as described herein,determining, based on image analysis of one or more images of anindividual, one or more personal attributes of the individual.Additionally, this may further include rendering the one or morepersonal attributes of the individual within an interactive GUI on or aspart of GUI 218. Result 222 (e.g., any of the one or more personalattributes) of such imaging analysis may be displayed on GUI 218. Inthis way, artificial intelligence based imaging system 100 is configuredto interact with individuals via a web environment. It is to be notedthat, as shown by FIG. 2, none of the digital images captured andanalyzed, are transmitted over any network (e.g., computer network 120)to server(s). In this way PII (e.g., the selfie of the individual) isnot at risk of being intercepted on a computer network, and, as aresult, increases the security of the artificial intelligence basedimaging system 100 and the client device 200 as a whole.

FIG. 3 illustrates a diagram of an example artificial intelligence basedimaging method 300 for interacting with individuals via a webenvironment in accordance with various embodiments disclosed herein. Atblock 302 artificial intelligence based imaging method 300 comprisesreceiving, at a provisioning server (e.g., provisioning server(s) 102),a request (e.g., a hypertext transfer (HTTP) request) from a web browser(e.g., web browser 210) for a predefined imaging code (e.g., predefinedimaging code 108). As described herein, the provisioning server (e.g.,provisioning server(s) 102) has access to a memory (e.g., database 105and/or memorie(s) 106) storing the predefined imaging code.

At block 304 artificial intelligence based imaging method 300 furthercomprises responding, by the provisioning server (e.g., provisioningserver(s) 102), to the request by transferring the predefined imagingcode (e.g., predefined imaging code 108) to the web browser (e.g., webbrowser 210), where the web browser is executing on a client device(e.g., client device 200). In various embodiments, web browser 210 isconfigured to receive hypertext markup language (HTML) from provisioningservers 102 for use in rendering at least a portion of a user interface(e.g., GUI 218) or display of web browser 210.

In various embodiments, predefined imaging code 108 is configured to bestored in a memory (e.g., memory 203) of the client device (e.g., clientdevice 200) by the web browser (e.g., web browser 210), for example,when predefined imaging code 108 is received by the web browser.Additionally or alternatively, in various embodiments, predefinedimaging code 108 is configured to be executed by a client processor(e.g., CPU 202) of the client device (e.g., client device 200) uponpredefined imaging code 108 being received by web browser 210.

At block 306 artificial intelligence based imaging method 300 furthercomprises rendering, with predefined imaging code 108, an interactivegraphical user interface (e.g., GUI 218) within the web browser (e.g.,web browser 210) on a display (e.g., display screen 201) of the clientdevice (e.g., client device 200).

At block 308 artificial intelligence based imaging method 300 furthercomprises loading, with the predefined imaging code (e.g., predefinedimaging code 108) into a memory (e.g., memory 203) of the client device,one or more images (e.g., as captured by camera 204) of an individual.

At block 310 artificial intelligence based imaging method 300 furthercomprises determining, with the predefined imaging code (e.g.,predefined imaging code 108) based on image analysis of the one or moreimages of the individual, one or more personal attributes of theindividual. In various embodiments, the image analysis comprisestraining and/or using a machine learning model, neural network, or otherartificial intelligence model, based on pixel data of the one or moreimages of the individual, to determine the one or more personalattributes of the individual. For example, in various embodiments, amachine learning imaging model, as described herein, may be trainedusing a supervised or unsupervised machine learning program oralgorithm. The machine learning program or algorithm may employ a neuralnetwork, which may be a convolutional neural network, a deep learningneural network, or a combined learning module or program that learns intwo or more features or feature datasets in a particular areas ofinterest. The machine learning programs or algorithms may also includenatural language processing, semantic analysis, automatic reasoning,regression analysis, support vector machine (SVM) analysis, decisiontree analysis, random forest analysis, K-Nearest neighbor analysis,naïve B ayes analysis, clustering, reinforcement learning, and/or othermachine learning algorithms and/or techniques. Machine learning mayinvolve identifying and recognizing patterns in existing data (such astraining a model based on pixel data within images having a plurality ofpersonal attributes of individuals) in order to facilitate makingpredictions or identification for subsequent data (such as using themodel on new pixel data of a new individual in order to determine newone or more personal attributes of such new individual).

Machine learning model(s), such as those of a machine learning imagingmodel described herein, may be created and trained based upon example(e.g., “training data,”) inputs or data (which may be termed “features”and “labels”) in order to make valid and reliable predictions for newinputs, such as testing level or production level data or inputs. Insupervised machine learning, a machine learning program operating on aserver, computing device, or otherwise processor(s), may be providedwith example inputs (e.g., “features”) and their associated, orobserved, outputs (e.g., “labels”) in order for the machine learningprogram or algorithm to determine or discover rules, relationships, orotherwise machine learning “models” that map such inputs (e.g.,“features”) to the outputs (e.g., labels), for example, by determiningand/or assigning weights or other metrics to the model across itsvarious feature categories. Such rules, relationships, or otherwisemodels may then be provided subsequent inputs in order for the model,executing on the server, computing device, or otherwise processor(s), topredict, based on the discovered rules, relationships, or model, anexpected output.

In unsupervised machine learning, the server, computing device, orotherwise processor(s), may be required to find its own structure inunlabeled example inputs, where, for example multiple trainingiterations are executed by the server, computing device, or otherwiseprocessor(s) to train multiple generations of models until asatisfactory model, e.g., a model that provides sufficient predictionaccuracy when given test level or production level data or inputs, isgenerated. The disclosures herein may use one or both of such supervisedor unsupervised machine learning techniques.

Image analysis may include training a machine learning imaging modelbased on pixel data of images of one or more individuals. Additionally,or alternatively, image analysis may include using a machine learningimaging model, as previously trained, to determine, based on the one ormore images of the individual, one or more personal attributes of theindividual. In this way, pixel data (e.g., detailing one or morefeatures of an individual, such as a mouth, hairline, face or portion(s)thereof) may be used to train or use a machine learning imaging model todetermine one or more personal attributes of the individual. Suchpersonal attributes may include, but are not limited to a facial feature(e.g., a skin or facial hair feature), an oral, e.g., tooth or teethrelated need (e.g., plaque, coloring, or other oral issue), or ahair-based feature (e.g., hair length, hair coloring, etc.) features ofthe individual.

In some embodiments, predefined imaging code 108, as transmitted toclient device 200, comprises a web assembly (WASM) based module and oneor more scripts (e.g., JavaScript code). In various embodiment, a WASMbased module comprises a machine learning imaging model (as describedherein). The machine learning imaging model is configured to input oneor more images of an individual and determine the personal attributes ofthe individual as described herein. In some embodiments, the one or morescripts or, more generally, predefined imaging code 108 may comprise amachine learning imaging model, where the machine learning imaging modelimplements or uses at least a TENSORFLOW based library. Additionally, oralternatively, machine learning imaging model may comprise, implement,or otherwise use other artificial intelligence based third-party packageor libraries including the PYTORCH library or the SCIKIT-LEARN Pythonlibrary.

Referring to FIG. 3, at block 312 artificial intelligence based imagingmethod 300 further comprises rendering, with the predefined imaging code(e.g., predefined imaging code 108), the one or more personal attributesof the individual within the interactive GUI (e.g., GUI 218). In variousembodiments, predefined imaging code 108 is further configured, uponexecution by the client processor (e.g., CPU 202), to render the one ormore images with the one or more personal attributes of the individualwithin an interactive GUI (e.g., GUI 218).

In various embodiments, predefined imaging code 108 is executable withinthe web browser (e.g., web browser 210) without communication with theprovisioning server (e.g., provisioning server(s) 102). In someembodiments, predefined imaging code 108 may be transferred during asingle transmission from a provisioning server (e.g., provisioningserver(s) 102) to the web browser (e.g., web browser 210). Additionally,or alternatively, predefined imaging code is executable within a webbrowser (e.g., web browser 210) without sending personally identifiableinformation (PII) to the provisioning server (e.g., provisioningserver(s) 102).

In various embodiments predefined imaging code 108 is cached in thememory (e.g., memory 203) of the client device (e.g., client device200), where the predefined imaging code 108 is allowed to access atleast a portion of the memory (e.g., memory 203) as allocated for theweb browser (e.g., web browser 210). Additionally, or alternatively, thepredefined imaging code 108 is allowed to access at least a portion ofthe memory (e.g., memory 203) as not allocated for the web browser(e.g., web browser 210), or that is otherwise separate from the memoryallocated for the web browser. In some embodiments, at least a portionof predefined imaging code 108 is cached in the memory (e.g., memory203) of the client device (e.g., client device 200) before or duringcapture of the one or more images of the individual.

FIG. 4 illustrates a diagram of a further example artificialintelligence based imaging method 400 for interacting with individualsvia a web environment in accordance with method 300 of FIG. 3, and inaccordance with various embodiments disclosed herein, and where one ormore personal attributes of an individual comprise one or more facialfeatures, one or more oral features, or one or more hair-based featuresof the individual.

Method 400 of FIG. 4 describes image analysis of an example image 500 ofFIG. 5. More generally, FIG. 5 illustrates image 500 for image analysisby any of the artificial intelligence based imaging systems or methodsas described for FIGS. 1-4, respectively, of an individual 501 showingone or more personal attributes 502-522 of individual 501 comprising oneor more facial features (e.g., skin 502, eye 504, eyebrow 506, lips512), one or more oral features (e.g., teeth or through (not shown)), orone or more hair-based features (e.g., hair 522) of individual 501, inaccordance with various embodiments disclosed herein. As illustrated byFIG. 5, image 500 comprises pixel data corresponding to each of thepersonal attributes 502-522, and their respective areas and/or locationswithin image 500 of individual 501. The pixel data of any of the one ormore personal attributes 502-522 may be captured by camera 204 andanalyzed by predefined imaging code 108, including, for example, bymachine learning imaging model(s), e.g., WASM module, as describedherein.

With reference to FIG. 4, at block 402 method 400 comprises clientdevice 200 sending, to a provisioning server (e.g., provisioning servers102), a request from a web browser (e.g., web browser 210) for apredefined imaging code (e.g., predefined imaging code 108).Provisioning server may access a memory (e.g., database 105 and/ormemorie(s) 106) storing predefined imaging code 108. In variousembodiments, the request may be made from a link within a web page, suchas an initial or preload webpage that requests software, code,instructions, or other such assets (e.g., predefined imaging code 108)to allow artificial intelligence based imaging analysis as describedherein. Additionally, or alternatively, the request may be made from apredefined link, e.g., such as a link or address stored in memory 203 ofclient device 200.

At block 404 method 400 further comprises the provisioning server (e.g.,provisioning server(s) 102) responding to the request by transferringpredefined imaging code 108 to the web browser (e.g., web browser 210executing on a client device 200). As illustrated by non-limitingexample for FIG. 4, a response from provisioning server(s) includessoftware, code, and/or instructions comprising hyper-text markuplanguage (HTML), JavaScript (js), cascading style sheet (CSS) script,and WASM code (e.g., the WASM code comprising the machine learning MLmodel for performing imaging analysis of image 500 and/or other imagesof the individual). For example, predefined imaging code 108 cancomprise such software, code, and/or instructions, as transferred inblock 406, in whole or in part. For example, predefined imaging code 108may comprise WASM code comprising a machine learning (ML) model butwhere the remaining portion of the response from provisioning server(s)102 may comprise JavaScript, such as proprietary code or third partycode (e.g., TensorFlow.js), and/or HTML and CSS for use by web browser210 to format, arrange, or generate an front end UI (e.g., UI 218) ondisplay screen 201 of client device 200. For example, in variousembodiments, web browser 210 is configured to receive hypertext markuplanguage (HTML) from provisioning servers 102 for use in rendering atleast a portion of a user interface (e.g., GUI 218) or display of webbrowser 210.

In various embodiments, predefined imaging code 108 is executable withinthe web browser (e.g., web browser 210) without communication with theprovisioning server (e.g., provisioning server(s) 102). In someembodiments, predefined imaging code 108 may be transferred during asingle transmission from a provisioning server (e.g., provisioningserver(s) 102) to the web browser (e.g., web browser 210). Additionally,or alternatively, predefined imaging code is executable within a webbrowser (e.g., web browser 210) without sending personally identifiableinformation (PII) to the provisioning server (e.g., provisioningserver(s) 102). In this way, WASM code may interact with the web browser210, including via HTML, JavaScript, or otherwise, to update web browser210 without needing to request processing or information fromprovisioning server(s) 102. Such interaction may include interactingwith the document object model (DOM) of a webpage of web browser 210,for example, causing text and/or images, e.g., e.g., image 500 andrelated text, to be displayed via GUI 218 of web browser 210.

At block 406, method 400 further comprises loading or storing predefinedimaging code 108 into cache or memory (e.g., memory 203) of, used by, orat least shared by web browser 210. In various embodiments, predefinedimaging code 108 is configured to be executed by a client processor(e.g., CPU 202) of a client device (e.g., client device 200) upon thepredefined imaging code 108 being received by the web browser (e.g., webbrowser 210). For example, as shown in FIG. 4, a JavaScript file (e.g.,that received by provisioning server(s) 102) may instruct web browser210 and/or client device 200 to store predefined imaging code 108 inmemory 203 of client device 200 when predefined imaging code 108 isreceived by the web browser 210.

At block 408, method 400 further comprises web browser 210 reading HTMLand CSS scripts, code, and/or instructions, such as those as received byprovisioning server(s) 102, to render GUI 218. That is, web browser 210execution on client device 200 may render, with predefined imaging code108 and/or HTML, JavaScript, CSS, or other instructions received byprovisioning server(s) 102, a GUI (e.g., GUI 218) within web browser 210on a display (e.g., web browser 210) of client device 200.

At block 410, a user, such as individual 501, may capture one or moreimages (e.g., image 500, such as a “selfie” image or frame) with camera204.

At block 412 method 400 may comprise image optimization of an image(e.g., image 500), such as updating a size, resolution, or cropping animage (e.g., image 500). In some embodiments, such optimization mayinvolve altering an area of interest (e.g., such as areas of one or morepersonal attributes 502-522) by updating, changing, selecting, and/orcropping pixels or areas of the image (e.g., image 500). Such imageoptimization may be performed by a JavaScript script or other code orinstruction as transferred from provisioning server(s) 102.

At block 420, method 400 further comprises loading, into a memory (e.g.,memory 203), a machine learning imaging model (e.g., a WASM basedmodel). This may include loading predefined imaging code 108 into memory203 in whole or in part. As described herein, predefined imaging code108 may comprise a WASM based module and one or more scripts. In variousembodiments, such scripts may include a JavaScript file for storing,loading, and/or configuring WASM based module for operation or executionon client device 200.

Predefined imaging code 108 may be cached in the memory (e.g., memory203) of the client device (e.g., client device 200), where thepredefined imaging code 108 is allowed to access at least a portion ofthe memory (e.g., memory 203) as allocated for the web browser (e.g.,web browser 210). As shown for FIG. 5, at least a portion of predefinedimaging code 108 is cached in the memory (e.g., memory 203) of theclient device (e.g., client device 200) before or during capture of theone or more images (e.g., image 500) of the individual (e.g., individual501).

At block 422 method 400 further comprises loading, with predefinedimaging code 108 into a memory (e.g., memory 203) of the client device(e.g., client device 200), one or more images (e.g., image 500) ofindividual 501. In various embodiments, JavaScript code or such otherinstructions as provided by predefined imaging code 108 may load theimages. As shown in FIG. 5, the one or more images may be provided to amachine learning imaging model for image analysis.

As illustrated by FIG. 4, a WASM based module can comprise a machinelearning imaging model. WASM based module may be executed by CPU 202 ofclient device 200 to perform image analysis of image 500. Image analysismay include training or updating machine learning imaging model based onpixel data of images (e.g., image 500) of one or more individuals (e.g.,individual 501). Additionally, or alternatively, image analysis mayinclude using a machine learning imaging model of WASM based module, aspreviously trained, to determine, based on the one or more images (e.g.,image 500) of the individual (e.g., individual 501), one or morepersonal attributes of the individual (e.g., personal attributes502-522). Pixel data of image 500 (e.g., detailing one or more featuresof an individual, such as a mouth, hairline, face, skin, or portion(s)thereof) may be used to train the WASM based machine learning imagingmodel, or cause an existing machine model, to determine one or morepersonal attributes of the individual. Such personal attributes mayinclude, but are not limited to a facial feature (e.g., a skin or facialhair feature), an oral, e.g., tooth or teeth related need (e.g., plaque,coloring, or other oral issue), or a hair-based feature (e.g., hairlength, hair coloring, etc.) features of the individual, for example asshown and described for image 500 of FIG. 5. The machine learning modelmay also be trained, and may also use, non-image or text based data ofan individual including, but not limited, demographic data (e.g., age,sex, etc.) and geolocation, humidity of an area, etc.

At block 424 method 400 further comprises determining, with predefinedimaging code 108 based on image analysis of the one or more images(e.g., image 500) of the individual (e.g., 500), one or more personalattributes (e.g., personal attributes 502-522) of individual 501. Forexample, this may include the result of a machine learning imagingmodel, such as a WASM based machine learning imaging model.

In various embodiments, a machine learning imaging model may input pixeldata of an image (e.g., image 500) of an individual (e.g., individual501) as feature data. The machine learning imaging model may output, asa result at block 424, label data, that may include numeric data (e.g.,age of individual 501), classification data (e.g., hair length withclassification “1” or “2” of personal attribute 522 and/or eye color“green” or “blue” of persona attribute 504), or identification data(e.g., skin area of personal attribute 502). Additionally, oralternatively, the machine learning imaging model may output, as aresult at block 424, label data, that may include a visualization orannotation. Such visualization or annotation may comprise an image orvideo image (e.g., image 500) highlighting possible areas of interest(e.g., personal attributes 502-522). In some embodiments, suchvisualization or annotation may be shown on display screen 201 inreal-time or near real-time, with the particular areas marked orhighlighted, as illustrated by the areas or portions of image 500 (e.g.,personal attributes 502-522) as shown by FIG. 5. In some embodiments,such the particular areas marked or highlighted may be shown as anaugmented reality (AR) image where the individual's (e.g., individual501) face, or other body portion, is shown in real-time or nearreal-time with annotated markings (e.g., personal attributes 502-522)superimposed on the image 500 and viewable on display screen 201.

At block 426, method 400 further comprises displaying the result, asgenerated or produced by machine learning imaging model, as describedfor block 424, on GUI 218. Client device 200, with the predefinedimaging code 108 (e.g., including scripts and or other instructionsdescribed herein), may render one or more images and related personalattributes (e.g., any one of 502-522) of individual 501 within theinteractive GUI (e.g., display screen 201). As described, the one ormore personal attributes 502-522 may comprise one or more facialfeatures (e.g., skin 502, eye 504, eyebrow 506, or lips 512), one ormore oral features (e.g., teeth (not shown)), or one or more hair-basedfeatures (e.g., hair 522) of individual 501. The images may be renderedas still images, video images (i.e., frames), annotated images, AR basedimages, or the like as described herein.

In various embodiment, the result or images as determined, or analyzed,may be displayed with, or be used to display (e.g., via display screen201), one or more corresponding recommend products. For example, arecommended product (e.g., makeup or base) that corresponds to age,wrinkles, eye color, hair color, length of hair, etc. as determined fromthe pixel data of the personal attributes (e.g., personal attributes502-522) of the individual may be displayed or otherwise recommended tothe user or individual.

FIG. 6 illustrates an example display or user interface 600 forrecommending one or more products (e.g., products 602 p and 622 p) basedon the one or more personal attributes (e.g., 502 and 522) of individual501 of FIG. 5. In the example of FIG. 6, user interface 600 may be, ormay be displayed as part of, user interface 218 described herein. Forexample, in various embodiments, the output or result of machinelearning imaging model (e.g., of and/or WASM module as described herein)may be used to generate or identify recommendations for correspondingproduct(s) on based on image analysis (e.g., analysis of pixel data ofimage 500) of the one or more personal attributes (e.g., 502 and 522) ofindividual 501. Such recommendations may include products such astoothpaste (e.g., for oral attributes) or how to use a particularproduct (e.g., how to apply a specific, recommended beauty cream). Otherexamples may include haircare, e.g., display of textual or videoinstructions of how to achieve a recommended hair grooming or ahairstyle for the user or individual using recommended products.

In the example of FIG. 6, user interface 600 comprises a list 601 ofrecommended products as determined by image analysis of image 500 and onthe one or more personal attributes (e.g., 502 and 522) of individual501 of FIG. 5. List 601 includes at least two products 602 p and 622 pthat correspond, respectively, to personal attributes 502 and 522. List601 includes a first recommendation 602 for personal attribute 502(e.g., a skin attribute), where the recommend product 602 p is a skincare product that may address one or more identified features (e.g.,wrinkles) of individual 501 as detected by a machine learning imagingmodel (e.g., of predefined imaging code 108) by analysis of image 500.

Similarly, list 601 includes a second recommendation 602 for personalattribute 522 (e.g., a hair attribute), where the recommend product 622p is a hair care product that may address one or more identifiedfeatures (e.g., split ends, dandruff, or hair shine) of individual 501as detected by a machine learning imaging model (e.g., of predefinedimaging code 108) by analysis of image 500. Other such products for thesame or different personal attributes, including those as describedherein, may be also be recommended in a similar manner.

User interface 600 may further include selectable UI buttons 602 s and622 s to allow a user (e.g., individual 501) to select for purchase orshipment the corresponding products, e.g., 602 p and 622 p,respectively. In some embodiments, selection of selectable UI buttons602 s and 622 s may cause the recommended product(s) to be shipped tothe user (e.g., individual 501) and/or may notify a third party that theindividual is interested in the product(s).

ASPECTS OF THE DISCLOSURE

The following aspects are provided as examples in accordance with thedisclosure herein and are not intended to limit the scope of thedisclosure.

1. An artificial intelligence based imaging system configured tointeract with individuals via a web environment, the artificialintelligence based imaging system comprising: a provisioning servercomprising one or more processors and one or more memories, theprovisioning server configured to respond to requests from a web browserexecuting on a client device; a predefined imaging code stored in theone or more memories of the server; and computing instructionsconfigured to execute on the one or more processors of the provisioningserver, wherein the computing instructions cause the provisioningserver, upon receiving a request from the web browser, to transfer thepredefined imaging code to the web browser, wherein the predefinedimaging code is configured to be stored in a memory of the client deviceby the web browser when the predefined imaging code is received by theweb browser, wherein the predefined imaging code is configured to beexecuted by a client processor of the client device upon the predefinedimaging code being received by the web browser, wherein, the predefinedimaging code is configured, upon execution by the client processor, to:render an interactive graphical user interface (GUI) within the webbrowser on a display of the client device, load, into a memory of theclient device, one or more images of an individual, determine, based onimage analysis of the one or more images of the individual, one or morepersonal attributes of the individual, and, render the one or morepersonal attributes of the individual within the interactive GUI,wherein the predefined imaging code is executable within the web browserwithout communication with the provisioning server, and wherein thepredefined imaging code is cached in the memory of the client device,the predefined imaging code allowed to access at least a portion of thememory as allocated for the web browser.

2. The artificial intelligence based imaging system of aspect 1, wherethe predefined imaging code comprises a web assembly (WASM) based moduleand one or more scripts.

3. The artificial intelligence based imaging system of aspect 2, whereinthe WASM based module comprises a machine learning imaging model, themachine learning imaging model configured to input the one or moreimages of the individual and determine the personal attributes of theindividual.

4. The artificial intelligence based imaging system of any one ofaspects 1-3, wherein the predefined imaging code comprises a machinelearning imaging model implements at least a TENS ORFLOW based library.

5. The artificial intelligence based imaging system of any one ofaspects 1-4, wherein the predefined imaging code is transferred during asingle transmission from the provisioning server to the web browser.

6. The artificial intelligence based imaging system of aspect 5, whereinat least a portion of the predefined imaging code is cached in thememory of the client device before or during capture of the one or moreimages of the individual.

7. The artificial intelligence based imaging system of any one ofaspects 1-6, wherein the web browser is configured to receive hypertextmarkup language (HTML).

8. The artificial intelligence based imaging system of any one ofaspects 1-7, wherein the predefined imaging code is executable withinthe web browser without sending personally identifiable information(PII) to the provisioning server.

9. The artificial intelligence based imaging system of any one ofaspects 1-8, wherein the predefined imaging code is further configured,upon execution by the client processor, to: render the one or moreimages with the one or more personal attributes of the individual withinthe interactive GUI.

10. An artificial intelligence based imaging method for interacting withindividuals via a web environment, the artificial intelligence basedimaging method comprising: receiving, at a provisioning server, arequest from a web browser for a predefined imaging code, theprovisioning server having access to a memory storing the predefinedimaging code; responding, by the provisioning server, to the request bytransferring the predefined imaging code to the web browser, the webbrowser executing on a client device, wherein the predefined imagingcode is configured to be stored in a memory of the client device by theweb browser when the predefined imaging code is received by the webbrowser, wherein the predefined imaging code is configured to beexecuted by a client processor of the client device upon the predefinedimaging code being received by the web browser; rendering, with thepredefined imaging code, an interactive graphical user interface (GUI)within the web browser on a display of the client device; loading, withthe predefined imaging code into a memory of the client device, one ormore images of an individual; determining, with the predefined imagingcode based on image analysis of the one or more images of theindividual, one or more personal attributes of the individual; andrendering, with the predefined imaging code, the one or more personalattributes of the individual within the interactive GUI, wherein thepredefined imaging code is executable within the web browser withoutcommunication with the provisioning server, and wherein the predefinedimaging code is cached in the memory of the client device, thepredefined imaging code allowed to access at least a portion of thememory as allocated for the web browser.

11. The artificial intelligence based imaging system of aspect 10, wherethe predefined imaging code comprises a web assembly (WASM) based moduleand one or more scripts

12. The artificial intelligence based imaging system of aspect 11,wherein the WASM based module comprises a machine learning imagingmodel, the machine learning imaging model configured to input the one ormore images of the individual and determine the personal attributes ofthe individual.

13. The artificial intelligence based imaging system of any one ofaspects 10-12, wherein the predefined imaging code comprises a machinelearning imaging model implements at least a TENS ORFLOW based library.

14. The artificial intelligence based imaging system of any one ofaspects 10-13, wherein the predefined imaging code is transferred duringa single transmission from the provisioning server to the web browser.

15. The artificial intelligence based imaging system of aspect 14,wherein at least a portion of the predefined imaging code is cached inthe memory of the client device before or during capture of the one ormore images of the individual.

16. The artificial intelligence based imaging system of any one ofaspects 10-15, wherein the web browser is configured to receivehypertext markup language (HTML).

17. The artificial intelligence based imaging system of any one ofaspects 10-16, wherein the predefined imaging code is executable withinthe web browser without sending personally identifiable information(PII) to the provisioning server.

18. The artificial intelligence based imaging system of any one ofaspects 10-17, wherein the predefined imaging code is furtherconfigured, upon execution by the client processor, to: render the oneor more images with the one or more personal attributes of theindividual within the interactive GUI.

19. An artificial intelligence based imaging system configured tointeract with individuals via a web environment, the artificialintelligence based imaging system comprising: a provisioning servercomprising one or more processors and one or more memories, theprovisioning server configured to respond to requests from a web browserexecuting on a client device; a predefined imaging code stored in theone or more memories of the server; and computing instructionsconfigured to execute on the one or more processors of the provisioningserver, wherein the computing instructions cause the provisioningserver, upon receiving a request from the web browser, to transfer thepredefined imaging code to the web browser, wherein the predefinedimaging code is configured to be stored in a memory of the client deviceby the web browser when the predefined imaging code is received by theweb browser, wherein the predefined imaging code is configured to beexecuted by a client processor of the client device upon the predefinedimaging code being received by the web browser, wherein, the predefinedimaging code is configured, upon execution by the client processor, to:render an interactive graphical user interface (GUI) within the webbrowser on a display of the client device, load, into a memory of theclient device, one or more images of an individual, determine, based onimage analysis of the one or more images of the individual, one or morepersonal attributes of the individual, and, render the one or morepersonal attributes of the individual within the interactive GUI,wherein the one or more personal attributes comprise one or more facialfeatures, one or more oral features, or one or more hair-based featuresof the individual.

20. The artificial intelligence based imaging system of aspect 19,wherein the predefined imaging code is further configured, uponexecution by the client processor, to recommend one or more products tothe individual based the one or more personal attributes of theindividual.

21. An artificial intelligence based imaging method for interacting withindividuals via a web environment, the artificial intelligence basedimaging method comprising: receiving, at a provisioning server, arequest from a web browser for a predefined imaging code, theprovisioning server having access to a memory storing the predefinedimaging code; responding, by the provisioning server, to the request bytransferring the predefined imaging code to the web browser, the webbrowser executing on a client device, wherein the predefined imagingcode is configured to be stored in a memory of the client device by theweb browser when the predefined imaging code is received by the webbrowser, wherein the predefined imaging code is configured to beexecuted by a client processor of the client device upon the predefinedimaging code being received by the web browser; rendering, with thepredefined imaging code, an interactive graphical user interface (GUI)within the web browser on a display of the client device; loading, withthe predefined imaging code into a memory of the client device, one ormore images of an individual; determining, with the predefined imagingcode based on image analysis of the one or more images of theindividual, one or more personal attributes of the individual; andrendering, with the predefined imaging code, the one or more personalattributes of the individual within the interactive GUI, wherein the oneor more personal attributes comprise one or more facial features, one ormore oral features, or one or more hair-based features of theindividual.

22. The artificial intelligence based imaging method of aspect 21,wherein the predefined imaging code is further configured, uponexecution by the client processor, to recommend one or more products tothe individual based the one or more personal attributes of theindividual.

ADDITIONAL CONSIDERATIONS

Although the disclosure herein sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the description is defined by the words of the claims set forthat the end of this patent and equivalents. The detailed description isto be construed as exemplary only and does not describe every possibleembodiment since describing every possible embodiment would beimpractical. Numerous alternative embodiments may be implemented, usingeither current technology or technology developed after the filing dateof this patent, which would still fall within the scope of the claims.

The following additional considerations apply to the foregoingdiscussion. Throughout this specification, plural instances mayimplement components, operations, or structures described as a singleinstance. Although individual operations of one or more methods areillustrated and described as separate operations, one or more of theindividual operations may be performed concurrently, and nothingrequires that the operations be performed in the order illustrated.Structures and functionality presented as separate components in exampleconfigurations may be implemented as a combined structure or component.Similarly, structures and functionality presented as a single componentmay be implemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a machine-readable medium or in a transmission signal) or hardware.In hardware, the routines, etc., are tangible units capable ofperforming certain operations and may be configured or arranged in acertain manner. In example embodiments, one or more computer systems(e.g., a standalone, client or server computer system) or one or morehardware modules of a computer system (e.g., a processor or a group ofprocessors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location, while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In otherembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

This detailed description is to be construed as exemplary only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. A person of ordinaryskill in the art may implement numerous alternate embodiments, usingeither current technology or technology developed after the filing dateof this application.

Those of ordinary skill in the art will recognize that a wide variety ofmodifications, alterations, and combinations can be made with respect tothe above described embodiments without departing from the scope of theinvention, and that such modifications, alterations, and combinationsare to be viewed as being within the ambit of the inventive concept.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s). Thesystems and methods described herein are directed to an improvement tocomputer functionality, and improve the functioning of conventionalcomputers.

The dimensions and values disclosed herein are not to be understood asbeing strictly limited to the exact numerical values recited. Instead,unless otherwise specified, each such dimension is intended to mean boththe recited value and a functionally equivalent range surrounding thatvalue. For example, a dimension disclosed as “40 mm” is intended to mean“about 40 mm.”

Every document cited herein, including any cross referenced or relatedpatent or application and any patent application or patent to which thisapplication claims priority or benefit thereof, is hereby incorporatedherein by reference in its entirety unless expressly excluded orotherwise limited. The citation of any document is not an admission thatit is prior art with respect to any invention disclosed or claimedherein or that it alone, or in any combination with any other referenceor references, teaches, suggests or discloses any such invention.Further, to the extent that any meaning or definition of a term in thisdocument conflicts with any meaning or definition of the same term in adocument incorporated by reference, the meaning or definition assignedto that term in this document shall govern.

While particular embodiments of the present invention have beenillustrated and described, it would be obvious to those skilled in theart that various other changes and modifications can be made withoutdeparting from the spirit and scope of the invention. It is thereforeintended to cover in the appended claims all such changes andmodifications that are within the scope of this invention.

What is claimed is:
 1. An artificial intelligence based imaging systemconfigured to interact with individuals via a web environment, theartificial intelligence based imaging system comprising: a provisioningserver comprising one or more processors and one or more memories, theprovisioning server configured to respond to requests from a web browserexecuting on a client device; a predefined imaging code stored in theone or more memories of the provisioning server; and computinginstructions configured to execute on the one or more processors of theprovisioning server, wherein the computing instructions cause theprovisioning server, upon receiving a request from the web browser, totransfer the predefined imaging code to the web browser, wherein thepredefined imaging code is configured to be stored in a memory of theclient device by the web browser when the predefined imaging code isreceived by the web browser, wherein the predefined imaging code isconfigured to be executed by a client processor of the client deviceupon the predefined imaging code being received by the web browser,wherein, the predefined imaging code is configured, upon execution bythe client processor, to: render an interactive graphical user interface(GUI) within the web browser on a display of the client device, load,into a memory of the client device, one or more images of an individual,determine, based on image analysis of the one or more images of theindividual, one or more personal attributes of the individual, and,render the one or more personal attributes of the individual within theinteractive GUI, wherein the predefined imaging code is executablewithin the web browser without communication with the provisioningserver, and wherein the predefined imaging code is cached in the memoryof the client device, the predefined imaging code allowed to access atleast a portion of the memory as allocated for the web browser.
 2. Theartificial intelligence based imaging system of claim 1, where thepredefined imaging code comprises a web assembly (WASM) based module andone or more scripts.
 3. The artificial intelligence based imaging systemof claim 2, wherein the WASM based module comprises a machine learningimaging model, the machine learning imaging model configured to inputthe one or more images of the individual and determine the personalattributes of the individual.
 4. The artificial intelligence basedimaging system of claim 1, wherein the predefined imaging code comprisesa machine learning imaging model implements at least a TENSORFLOW basedlibrary.
 5. The artificial intelligence based imaging system of claim 1,wherein the predefined imaging code is transferred during a singletransmission from the provisioning server to the web browser.
 6. Theartificial intelligence based imaging system of claim 5, wherein atleast a portion of the predefined imaging code is cached in the memoryof the client device before or during capture of the one or more imagesof the individual.
 7. The artificial intelligence based imaging systemof claim 1, wherein the web browser is configured to receive hypertextmarkup language (HTML).
 8. The artificial intelligence based imagingsystem of claim 1, wherein the predefined imaging code is executablewithin the web browser without sending personally identifiableinformation (PII) to the provisioning server.
 9. The artificialintelligence based imaging system of claim 1, wherein the predefinedimaging code is further configured, upon execution by the clientprocessor, to: render the one or more images with the one or morepersonal attributes of the individual within the interactive GUI.
 10. Anartificial intelligence based imaging method for interacting withindividuals via a web environment, the artificial intelligence basedimaging method comprising: receiving, at a provisioning server, arequest from a web browser for a predefined imaging code, theprovisioning server having access to a memory storing the predefinedimaging code; responding, by the provisioning server, to the request bytransferring the predefined imaging code to the web browser, the webbrowser executing on a client device, wherein the predefined imagingcode is configured to be stored in a memory of the client device by theweb browser when the predefined imaging code is received by the webbrowser, wherein the predefined imaging code is configured to beexecuted by a client processor of the client device upon the predefinedimaging code being received by the web browser; rendering, with thepredefined imaging code, an interactive graphical user interface (GUI)within the web browser on a display of the client device; loading, withthe predefined imaging code into a memory of the client device, one ormore images of an individual; determining, with the predefined imagingcode based on image analysis of the one or more images of theindividual, one or more personal attributes of the individual; andrendering, with the predefined imaging code, the one or more personalattributes of the individual within the interactive GUI, wherein thepredefined imaging code is executable within the web browser withoutcommunication with the provisioning server, and wherein the predefinedimaging code is cached in the memory of the client device, thepredefined imaging code allowed to access at least a portion of thememory as allocated for the web browser.
 11. The artificial intelligencebased imaging method of claim 10, where the predefined imaging codecomprises a web assembly (WASM) based module and one or more scripts.12. The artificial intelligence based imaging method of claim 11,wherein the WASM based module comprises a machine learning imagingmodel, the machine learning imaging model configured to input the one ormore images of the individual and determine the personal attributes ofthe individual.
 13. The artificial intelligence based imaging method ofclaim 10, wherein the predefined imaging code comprises a machinelearning imaging model implements at least a TENSORFLOW based library.14. The artificial intelligence based imaging method of claim 10,wherein the predefined imaging code is transferred during a singletransmission from the provisioning server to the web browser.
 15. Theartificial intelligence based imaging method of claim 14, wherein atleast a portion of the predefined imaging code is cached in the memoryof the client device before or during capture of the one or more imagesof the individual.
 16. The artificial intelligence based imaging methodof claim 10, wherein the web browser is configured to receive hypertextmarkup language (HTML).
 17. The artificial intelligence based imagingmethod of claim 10, wherein the predefined imaging code is executablewithin the web browser without sending personally identifiableinformation (PII) to the provisioning server.
 18. The artificialintelligence based imaging method of claim 10, wherein the predefinedimaging code is further configured, upon execution by the clientprocessor, to: render the one or more images with the one or morepersonal attributes of the individual within the interactive GUI.
 19. Anartificial intelligence based imaging system configured to interact withindividuals via a web environment, the artificial intelligence basedimaging system comprising: a provisioning server comprising one or moreprocessors and one or more memories, the provisioning server configuredto respond to requests from a web browser executing on a client device;a predefined imaging code stored in the one or more memories of theprovisioning server; and computing instructions configured to execute onthe one or more processors of the provisioning server, wherein thecomputing instructions cause the provisioning server, upon receiving arequest from the web browser, to transfer the predefined imaging code tothe web browser, wherein the predefined imaging code is configured to bestored in a memory of the client device by the web browser when thepredefined imaging code is received by the web browser, wherein thepredefined imaging code is configured to be executed by a clientprocessor of the client device upon the predefined imaging code beingreceived by the web browser, wherein, the predefined imaging code isconfigured, upon execution by the client processor, to: render aninteractive graphical user interface (GUI) within the web browser on adisplay of the client device, load, into a memory of the client device,one or more images of an individual, determine, based on image analysisof the one or more images of the individual, one or more personalattributes of the individual, and, render the one or more personalattributes of the individual within the interactive GUI, wherein thepredefined imaging code is executable within the web browser withoutcommunication with the provisioning server, and wherein the one or morepersonal attributes comprise one or more facial features, one or moreoral features, or one or more hair-based features of the individual. 20.The artificial intelligence based imaging system of claim 19, whereinthe predefined imaging code is further configured, upon execution by theclient processor, to recommend one or more products to the individualbased the one or more personal attributes of the individual.
 21. Anartificial intelligence based imaging method for interacting withindividuals via a web environment, the artificial intelligence basedimaging method comprising: receiving, at a provisioning server, arequest from a web browser for a predefined imaging code, theprovisioning server having access to a memory storing the predefinedimaging code; responding, by the provisioning server, to the request bytransferring the predefined imaging code to the web browser, the webbrowser executing on a client device, wherein the predefined imagingcode is configured to be stored in a memory of the client device by theweb browser when the predefined imaging code is received by the webbrowser, wherein the predefined imaging code is configured to beexecuted by a client processor of the client device upon the predefinedimaging code being received by the web browser; rendering, with thepredefined imaging code, an interactive graphical user interface (GUI)within the web browser on a display of the client device; loading, withthe predefined imaging code into a memory of the client device, one ormore images of an individual; determining, with the predefined imagingcode based on image analysis of the one or more images of theindividual, one or more personal attributes of the individual; andrendering, with the predefined imaging code, the one or more personalattributes of the individual within the interactive GUI, wherein thepredefined imaging code is executable within the web browser withoutcommunication with the provisioning server, and wherein the one or morepersonal attributes comprise one or more facial features, one or moreoral features, or one or more hair-based features of the individual. 22.The artificial intelligence based imaging method of claim 21, whereinthe predefined imaging code is further configured, upon execution by theclient processor, to recommend one or more products to the individualbased the one or more personal attributes of the individual.